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---
library_name: transformers
license: gemma
base_model: vidore/colpaligemma-3b-pt-448-base
tags:
- colpali
- generated_from_trainer
model-index:
- name: finetune_colpali_v1_2-german_ver2-4bit
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetune_colpali_v1_2-german_ver2-4bit
This model is a fine-tuned version of [vidore/colpaligemma-3b-pt-448-base](https://huggingface.co/vidore/colpaligemma-3b-pt-448-base) on the German_docx dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0559
- Model Preparation Time: 0.0099
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time |
|:-------------:|:------:|:----:|:---------------:|:----------------------:|
| No log | 0.0816 | 1 | 0.3744 | 0.0099 |
| 1.6073 | 0.8163 | 10 | 0.3027 | 0.0099 |
| 1.2318 | 1.6327 | 20 | 0.2157 | 0.0099 |
| 0.6498 | 2.4490 | 30 | 0.1428 | 0.0099 |
| 0.5073 | 3.2653 | 40 | 0.1181 | 0.0099 |
| 0.5106 | 4.0816 | 50 | 0.1069 | 0.0099 |
| 0.2965 | 4.8980 | 60 | 0.0969 | 0.0099 |
| 0.3175 | 5.7143 | 70 | 0.0922 | 0.0099 |
| 0.1775 | 6.5306 | 80 | 0.1089 | 0.0099 |
| 0.1966 | 7.3469 | 90 | 0.0649 | 0.0099 |
| 0.1317 | 8.1633 | 100 | 0.0477 | 0.0099 |
| 0.1287 | 8.9796 | 110 | 0.0503 | 0.0099 |
| 0.2576 | 9.7959 | 120 | 0.0559 | 0.0099 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.1
- Datasets 3.1.0
- Tokenizers 0.20.1
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